3D Point Cloud Classification
126 papers with code • 5 benchmarks • 6 datasets
Image: Qi et al
Benchmarks
These leaderboards are used to track progress in 3D Point Cloud Classification
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Libraries
Use these libraries to find 3D Point Cloud Classification models and implementations
ModelNet
ScanObjectNN
ModelNet40-C
IntrA
Sydney Urban Objects
Teeth3DS
Subtasks
Most implemented papers
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
yanx27/Pointnet_Pointnet2_pytorch
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NeurIPS 2017
By exploiting metric space distances, our network is able to learn local features with increasing contextual scales.
Point Transformer
Pointcept/Pointcept
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ICCV 2021
For example, on the challenging S3DIS dataset for large-scale semantic scene segmentation, the Point Transformer attains an mIoU of 70. 4% on Area 5, outperforming the strongest prior model by 3. 3 absolute percentage points and crossing the 70% mIoU threshold for the first time.
Dynamic Graph CNN for Learning on Point Clouds
WangYueFt/dgcnn
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Point clouds provide a flexible geometric representation suitable for countless applications in computer graphics; they also comprise the raw output of most 3D data acquisition devices.
PointCNN: Convolution On $\mathcal{X}$-Transformed Points
yangyanli/PointCNN
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NeurIPS 2018
The proposed method is a generalization of typical CNNs to feature learning from point clouds, thus we call it PointCNN.
PCT: Point cloud transformer
MenghaoGuo/PCT
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It is inherently permutation invariant for processing a sequence of points, making it well-suited for point cloud learning.
Perceiver: General Perception with Iterative Attention
deepmind/deepmind-research
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The perception models used in deep learning on the other hand are designed for individual modalities, often relying on domain-specific assumptions such as the local grid structures exploited by virtually all existing vision models.
PointConv: Deep Convolutional Networks on 3D Point Clouds
DylanWusee/pointconv
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CVPR 2019
Besides, our experiments converting CIFAR-10 into a point cloud showed that networks built on PointConv can match the performance of convolutional networks in 2D images of a similar structure.
KPConv: Flexible and Deformable Convolution for Point Clouds
HuguesTHOMAS/KPConv
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ICCV 2019
Furthermore, these locations are continuous in space and can be learned by the network.
Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions
Deep neural networks on 3D point cloud data have been widely used in the real world, especially in safety-critical applications.
Relation-Shape Convolutional Neural Network for Point Cloud Analysis
Yochengliu/Relation-Shape-CNN
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CVPR 2019
Specifically, the convolutional weight for local point set is forced to learn a high-level relation expression from predefined geometric priors, between a sampled point from this point set and the others.
